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Pregled bibliografske jedinice broj: 1219760

Finding the most representative Latent Dirichlet Allocation run for topic modelling


Gusić Munđar, Jelena; Rako, Sabina; Šlibar, Barbara
Finding the most representative Latent Dirichlet Allocation run for topic modelling // Book of abstracts, 19th International conference on operational research KOI 2022 / Mijač, Tea ; Šestanović, Tea (ur.).
Zagreb, 2022. str. 117-117 (predavanje, međunarodna recenzija, sažetak, znanstveni)


CROSBI ID: 1219760 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Finding the most representative Latent Dirichlet Allocation run for topic modelling

Autori
Gusić Munđar, Jelena ; Rako, Sabina ; Šlibar, Barbara

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book of abstracts, 19th International conference on operational research KOI 2022 / Mijač, Tea ; Šestanović, Tea - Zagreb, 2022, 117-117

Skup
19th International Conference on Operational Research (KOI 2022)

Mjesto i datum
Šibenik, Hrvatska, 28.09.2022. - 30.09.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
learning analytics ; topic modelling ; Latent Dirichlet Allocation ; stability of allocation

Sažetak
The number of research publications is growing exponentially making the extraction of meaningful information increasingly challenging. Natural language processing may provide a solution. Latent Dirichlet Allocation (LDA) is frequently used to detect topics in a corpus of documents. It relies on Monte Carlo methods for estimation, which introduces a replicability risk. Recently, an approach to stabilization of topic- term allocation was proposed and implemented in R LDAPrototype package. Stabilization is achieved by analysing the topic-term frequency matrices from a set of LDA replications and choosing the LDA replication that is the most representative for the set. Another approach might be to base the choice of the most representative LDA replication on the document-topic frequency matrices. The objective of this research is to compare the two approaches to stabilization of LDA results on a corpus of papers on learning analytics and educational data mining.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne društvene znanosti



POVEZANOST RADA


Projekti:
HRZZ-IP-2020-02-5071 - Podizanje zrelosti visokih učilišta za implementaciju analitika učenja (HELA) (Begičević Ređep, Nina, HRZZ - 2020-02) ( CroRIS)

Ustanove:
Fakultet organizacije i informatike, Varaždin

Profili:

Avatar Url Sabina Rako (autor)

Avatar Url Barbara Šlibar (autor)

Avatar Url Jelena Gusić Munđar (autor)


Citiraj ovu publikaciju:

Gusić Munđar, Jelena; Rako, Sabina; Šlibar, Barbara
Finding the most representative Latent Dirichlet Allocation run for topic modelling // Book of abstracts, 19th International conference on operational research KOI 2022 / Mijač, Tea ; Šestanović, Tea (ur.).
Zagreb, 2022. str. 117-117 (predavanje, međunarodna recenzija, sažetak, znanstveni)
Gusić Munđar, J., Rako, S. & Šlibar, B. (2022) Finding the most representative Latent Dirichlet Allocation run for topic modelling. U: Mijač, T. & Šestanović, T. (ur.)Book of abstracts, 19th International conference on operational research KOI 2022.
@article{article, author = {Gusi\'{c} Mun\djar, Jelena and Rako, Sabina and \v{S}libar, Barbara}, year = {2022}, pages = {117-117}, keywords = {learning analytics, topic modelling, Latent Dirichlet Allocation, stability of allocation}, title = {Finding the most representative Latent Dirichlet Allocation run for topic modelling}, keyword = {learning analytics, topic modelling, Latent Dirichlet Allocation, stability of allocation}, publisherplace = {\v{S}ibenik, Hrvatska} }
@article{article, author = {Gusi\'{c} Mun\djar, Jelena and Rako, Sabina and \v{S}libar, Barbara}, year = {2022}, pages = {117-117}, keywords = {learning analytics, topic modelling, Latent Dirichlet Allocation, stability of allocation}, title = {Finding the most representative Latent Dirichlet Allocation run for topic modelling}, keyword = {learning analytics, topic modelling, Latent Dirichlet Allocation, stability of allocation}, publisherplace = {\v{S}ibenik, Hrvatska} }




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